RESPOND -Resilience Enhancement through Simulation, Prediction and Optimisation of Network Disruptions

RESPOND combines real-time and historical mobility data, digital twins and an open multimodal network model with AI in one auditable workflow. Discriminative AI detects anomalies and estimates passenger impact across modes; generative AI drafts evidence-grounded response plans, passenger information and preventive maintenance recommendations; authorised staff approve every action. A governance layer defines who holds the shared situational picture and who acts when, supported by contingency guidelines, emergency exercises and staff training. The solution is validated at TRL 6–7 in three pilots, each with at least three modes: commuter access across the Rhine to the BASF site in Rhein-Neckar (DE), the Digital Twin Evropská in Prague (CZ) and urban bus resilience in Santander (ES), with a training toolkit developed in Italy. Together they cover urban, suburban and long-distance travel and all requirements of the call. Targets: at least 20% lower passenger delay during planned disruptions and 40% faster dissemination of response plans during unexpected events. RESPOND delivers open-source components aligned with the European Mobility Data Space and a transferable governance template for other European regions and critical sites.